首次系统研究文本生成视频模型对硬件故障的鲁棒性，发现单次故障可导致语义错误。
On the Resilience of Text-to-Video Diffusion Models to Hardware Faults
- 通过注入计算与内存故障，测试三款文本到视频扩散模型的可靠性。
- 单次故障使性能下降最高达3.7%，内存故障比计算故障更致命。
- 7-28%的故障引发可见伪影，甚至改变视频语义内容，适合关注AI系统可靠性的研究者。
我们首次系统研究了文本到视频（T2V）扩散模型在随机硬件级故障下的鲁棒性。尽管T2V模型因其生成高质量、时序连贯且逼真视频的能力而被广泛用于自动化视频生成，但其迭代去噪过程和时空依赖性引入了独特的故障模式。我们在三个T2V模型和一个代表性基准上进行了广泛的故障注入研究，覆盖计算与内存故障。结果表明：（1）单次故障可使整体性能下降高达3.7%，其中语义正确性比感知质量受影响更严重；（2）内存故障比计算故障更具破坏性，高阶指数位尤为脆弱，广泛使用的bfloat16比其他格式更易受攻击；（3）7%-28%的故障会导致可见伪影，包括新增物体等语义改变，说明单次故障足以改变输出语义。研究揭示了部署中T2V系统的可靠性风险，并推动进一步提升容错能力的研究。代码已开源。
原文摘要 · Abstract (English)
We present the first systematic study of the resilience of text-to-video (T2V) diffusion models under random hardware-level faults. While T2V models are widely used for automated video generation due to their ability to produce high-quality, temporally coherent, and realistic videos, their iterative denoising process and spatiotemporal dependencies introduce unique failure modes. We perform an extensive fault-injection study covering both computational and memory faults across three T2V models and a representative benchmark. Our results show that (1) a single fault can degrade overall performance by up to 3.7\%, with semantic correctness more affected than perceptual quality; (2) memory faults are more damaging than computational faults, high-order exponent bits are particularly vulnerable, and the widely-used bfloat16 is more susceptible than alternative formats; and (3) 7-28\% of faults cause visible artifacts, including semantic changes such as added objects, suggesting that single faults are sufficient to alter output semantics. Our findings reveal reliability risks in deployed T2V systems and motivate further research on improving fault resilience. Code: \href{https://github.com/ztcoalson/T2V-Resilience}{https://github.com/ztcoalson/T2V-Resilience}.
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